Xingni Wang

Papers

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About

Xingni Wang is a leading researcher in robotics and intelligent control systems, with a primary focus on path planning and optimization algorithms for autonomous navigation. Her most impactful work, the 2025 paper "Hybrid path planning algorithm for robots based on modified golden jackal optimization method and dynamic window method," has already garnered 9 citations, reflecting its immediate influence in the field. Wang’s key contribution lies in developing a novel hybrid approach that integrates a modified golden jackal optimization (GJO) technique with the dynamic window method, significantly enhancing robot trajectory efficiency and obstacle avoidance in complex environments. This work addresses critical challenges in real-time navigation, balancing global path optimization with local reactive control. By refining bio-inspired algorithms, Wang has advanced the practical deployment of autonomous systems in dynamic settings, such as warehouse logistics and search-and-rescue operations. Her research bridges theoretical optimization and applied robotics, offering scalable solutions for multi-robot coordination. With a growing citation record, Xingni Wang is recognized for pushing the boundaries of intelligent motion planning, making her a rising figure in the robotics community.